```{r}
data = read.csv("demo/data/mettu_1972.csv")
data_long = read.csv("demo/data/mettu_1972_long.csv")
```Ethiopian Community Gathering
July 13, 2025
ChatGPT (Nov 2022)
State-of-the-Art (SOTA) Large Language Models (LLMs) are powerful and feature-rich
Rich builtin features
Customizing llms (“context”: adding knowledge)
\rightarrow Endless use cases (tutor, career coach, …)
My use cases
Improving writing, and fixing grammar mistakes/typos
Example
here are my questions 1,How could I wine the scholarship regarding your experience 2, What are the easy ways to get scholarships 3,If you have Templetes of documention prepartion to prepare my owen 4,If you Know sites to Apply scholarships 5,If you have any options which you advise me to get this chance.Thanks!!!
Write a cover letter:
Prompt
You'll primarily act as a career coach, helping me write tailored cover letters based on the context provided in this project: CV, motivation statement, and dissertation summary. I'll provide the job description for each position. Please sound less robotic and avoid clichés, usual, flashy words and phrases. You do this task when I use the flag @cl.
Occasionally, you'll be asked to answer application form questions such as "Why this role?" In such cases, use the information provided in the project context to formulate appropriate responses. The flag for this task is @answer.
If you notice that I’m not eligible or a good fit for the position for any reason, please state it upfront.
PROVIDE ALL YOUR COMMENTARY AT THE BEGINNING. NO COMMENTARY SHOULD BE ADDED AFTER THE COVER LETTER.
<LINKS>
1. Project 1: https://github.com/eyayaw/housing-supply-elasticity-in-germany
2. Project 2: https://github.com/eyayaw/de-donut-effect
3. Project 3: https://github.com/eyayaw/the-monocentric-city-gradients-addis-ababa
4. Dissertation: https://github.com/eyayaw/dissertation
</LINKS>
<STYLE_GUIDE>
1. Use American English
2. DON'T USE punctuation inside quotations like <"Professionalism," and ...>, rather use <"Professionalism", and ...>.
3. MINIMIZE the use of bullet points and em dashes
</STYLE_GUIDE>
Please embed links, for research projects and the dissertation repo, in markdown format. If asked explicitly to output Typst, use this format #link("https://github.com/eyayaw/housing-supply-elasticity-in-germany")[housing supply elasticity].Research Assistant
Browses the web and analyzes various sources on your behalf, assist you with your in-depth research.
Question❓
Have you called llms outside of their chat interfaces?
Category, Ethiopia, USA
Sq. Miles, 489239, 3608787
Population, 25008631, 205000000
Hospitals, 88 hospitals with 9449 beds,
Health Centers, 81,
Health Stations, 542 (bush clinics),| Category | Ethiopia | USA |
|---|---|---|
| Sq. Miles | 489239 | 3608787 |
| Population | 25008631 | 205000000 |
| Hospitals | 88 hospitals with 9449 beds | NA |
| Health Centers | 81 | NA |
| Health Stations | 542 (bush clinics) | NA |
| country | area | pop | hospitals | health_centers | health_stations | beds |
|---|---|---|---|---|---|---|
| Ethiopia | 489239 | 25008631 | 88 | 81 | 542 | 9449 |
| USA | 3608787 | 205000000 | NA | NA | NA | NA |
Unstructured data \rightarrow Structured output
Gemini API: Provide a schema (responseSchema), or in a text prompt
{
"property_type": "house",
"property_use": "residential",
"listing_type": "rent",
"price": {
"value": 140000.0,
"currency": "ETB",
"unit": "per month"
},
"address": {
"original": "ሲኤምሲ ኮምፓውንድ",
"transliterated": "CMC Compound"
},
"size": {
"plot_area": 600.0,
"unit": "sqm"
},
"property_condition": "excellent",
"bedrooms": 6,
"bathrooms": 5,
"furnishing_status": "fully-furnished",
"structural_features": {
"has_parking": true,
"parking_spaces": 6,
"has_garden": true
},
"seller": {
"type": "broker",
"contact": {
"phone": "0911067686"
},
"commission": 10.0
},
"description": "Fully equipped luxury residential house for rent located inside CMC compound. The property has 6 bedrooms, 5
bathrooms, 2 living rooms, and 2 kitchens. It includes ample garden space and parking for 6 vehicles.",
"remarks": "The advertisement explicitly mentions 2 living rooms and 2 kitchens, which are notable features."
}AI Playgrounds:
Code CLIs (programming)
Humanity’s Last Exam, a multi-modal benchmark at the frontier of human knowledge, 2,500 challenging questions across over 100 subjects. (Dataset)
MMLU – Measuring Massive Multitask Language Understanding (Dataset)
57 subjects (STEM, law, etc.), 16k multiple-choice questions, checks breadth of factual knowledge and multiple-choice reasoning accuracy.
BIG-Bench (+ variants): 204 tasks, diverse topics (including tasks which quantify social bias in language models) (Repo, BBH), task contribution from the community
ARC Challenge “ARC-AGI is the only AI benchmark that tests for general intelligence by testing not just for skill, but for skill acquisition.”
LongBench v2: Benchmarking deeper understanding and reasoning on realistic long-context multitasks. (See also Needle-in-a-Haystack)
Contexts up to 2 M words across QA, summarisation, code-repo understanding; probes long-context recall, retrieval and deep reasoning.